Imagine an AI that could automatically scan through mountains of code, sniffing out hidden vulnerabilities before hackers do. That's the promise of using large language models (LLMs) for vulnerability detection. A new research paper introduces "eyeballvul," a massive, constantly updated benchmark designed to test how well LLMs can find security flaws in real-world code. The benchmark uses real vulnerabilities from open-source projects, creating a realistic testing ground for LLMs. It's huge—containing over 24,000 vulnerabilities across thousands of code revisions—and keeps growing weekly. The research uses a clever trick: they feed chunks of code to the LLM and ask it to identify potential issues. Then, another LLM acts as a judge, comparing the AI's findings to the known vulnerabilities. Early results show promise, but there's still a long way to go. While LLMs are good at spotting surface-level issues like injection flaws, they struggle with more complex problems like memory corruption. Plus, they generate a lot of false positives, which can waste developers' time. The real cost isn't running the AI, but chasing down phantom bugs. This means future research needs to focus on reducing those false alarms and digging deeper into the code for hidden dangers. The paper suggests using specialized tools or even letting the LLM interact with the code to investigate further. The "eyeballvul" benchmark isn't just about testing today's AI; it’s designed to evaluate future generations of even more powerful models. It raises crucial questions about how these models are trained and whether they might have already learned about the vulnerabilities they're supposed to find. As AI evolves, benchmarks like this will play a key role in making sure our code is safe and secure.